Out-of-sequence-measurement processing for probabilistic multiple hypothesis tracker with measurement reordering

In a multi-sensor central level tracking system, owing to random delay in transmission and varying preprocessing time for different sensor platforms, an earlier measurement from the same target can arrive at the fusion center after a later one. Practical data fusion schemes are challenged by the inevitable appearance of measurements that are out of sequence, called, “out-of-sequence measurements” (OOSMs). The question is how to incorporate these OOSMs in a track that has already been updated with a later observation in order to enhance the performance of the tracking system. Several approaches for a sequential algorithm to find a solution for the OOSM problem have been discussed in previous papers. An approach to address the OOSM problem in the probabilistic multi-hypothesis tracker (PMHT), being a batch algorithm, was proposed in previous paper. However, the situation of this approach was not an OOSM case but, rather, an out of sequence scan (OOSS) where a batch of data was lost and then only one scan of measurements from the lost batch arrived with the present batch. In this paper, we propose an approach that has a measurement reordering step to address the OOSM problem in the PMHT within the framework of the OOSM case and report on the performance with the simulation results. The simulation results indicate that the proposed approach may be a suitable solution for the OOSM problem in PMHT under the proper conditions of length of batch, amount of lag, density of clutter, and probability of detection for the target.     

基于词素的日文分词方法及其在OCR系统中的应用

在基于OCR技术的大规模文档录入系统中,自动检错可以大大降低人工校对成本。在日文OCR系统自动检错中,日文单词因其动词及形容词、形容动词的词尾变化现象使自动分词变得比较困难。本文提出了一种基于词素的日文分词新方法,通过建立以词素为基础单位的分词词库,以最大长度优先词条匹配方法分割出文章中有词尾变化的日文单词,避免了传统日文分词中收录单词各种词尾变化形式造成分词词库过于庞大的问题。实验表明,本文提出的分词方法可以达到99.0%的分词正确率;将该方法运用在OCR检错模块,当系统拒识率(即检错模块中认为可疑的字符在总字符中的比例)控制在1/5时,测试集上漏检率为0.05%,说明了该方法的有效性。     

利用结对编程技术改进程序设计实践教学

介绍了结对编程的概念,阐述了其作为学习编程技能的一种过程改进策略在国外计算机专业教学中的实验效果,并论述了相关影响因素。     

Ferret: Programming language support for multiple dynamic classification

We introduce a concept of multiple dynamic classification, a powerful generalization of single-inheritance OO, and a language Ferret which implements it. Multiple classification allows Male, Female, and Married to be subclasses of Person, arranged so that a single Person object may be both Male and Married, but may not be both Male and Female. Dynamic classification allows classes to change: a Person may acquire or lose Married status.The subclasses are true subclasses. Married carries fields (e.g., spouse) which are specific to married people. Methods may be defined on classes, and even on Boolean combinations of class: Male & Married. Ferret provides a generalization of superclass calls, so that the methods for Male &  Married can be based on those for Male and Married, without losing other classifications like Employee. Ferret has mutators, analogous to constructors but applicable when objects change class. The resulting language is powerful and highly expressive.     

Integrating multiple document features in language models for expert finding

We argue that expert finding is sensitive to multiple document features in an organizational intranet. These document features include multiple levels of associations between experts and a query topic from sentence, paragraph, up to document levels, document authority information such as the PageRank, indegree, and URL length of documents, and internal document structures that indicate the experts’ relationship with the content of documents. Our assumption is that expert finding can largely benefit from the incorporation of these document features. However, existing language modeling approaches for expert finding have not sufficiently taken into account these document features. We propose a novel language modeling approach, which integrates multiple document features, for expert finding. Our experiments on two large scale TREC Enterprise Track datasets, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios. Our main contribution is to develop an effective formal method for modeling multiple document features in expert finding, and conduct a systematic investigation of their effects. It is worth noting that our novel approach achieves better results in terms of MAP than previous language model based approaches and the best automatic runs in both the TREC2006 and TREC2007 expert search tasks, respectively.     

A pattern reordering approach based on ambiguity detection for online category learning

Pattern reordering is proposed as an alternative to sequential and batch processing for online category learning. Upon detecting that the categorization of a new input pattern is ambiguous, the input is postponed for a predefined time, after which it is reexamined and categorized for good. This approach is shown to improve the categorization performance over purely sequential processing, while yielding a shorter input response time, or latency, than batch processing. In order to examine the response time of processing schemes, the latency of a typical implementation is derived and compared to lower bounds. Gaussian and softmax models are derived from reject option theory and are considered for detecting ambiguity and triggering pattern postponement. The average latency and Rand Adjusted clustering score of reordered, sequential, and batch processing are compared through computer simulation using two unsupervised competitive learning neural networks and a radar pulse data set.     

Integration of multiple acoustic and language models for improved Hindi speech recognition system

Despite the significant progress of automatic speech recognition (ASR) in the past three decades, it could not gain the level of human performance, particularly in the adverse conditions. To improve the performance of ASR, various approaches have been studied, which differ in feature extraction method, classification method, and training algorithms. Different approaches often utilize complementary information; therefore, to use their combination can be a better option. In this paper, we have proposed a novel approach to use the best characteristics of conventional, hybrid and segmental HMM by integrating them with the help of ROVER system combination technique. In the proposed framework, three different recognizers are created and combined, each having its own feature set and classification technique. For design and development of the complete system, three separate acoustic models are used with three different feature sets and two language models. Experimental result shows that word error rate (WER) can be reduced about 4% using the proposed technique as compared to conventional methods. Various modules are implemented and tested for Hindi Language ASR, in typical field conditions as well as in noisy environment.     

An optical burst reordering model for time-based and random selection assembly strategies

Contention resolution schemes in optical burst switched networks (OBS) as well as contention avoidance schemes delay burst delivery and change the burst arrival sequence. The burst arrival sequence usually changes the packet arrival sequence and degrades the upper layer protocols performance, e.g., the throughput of the transmission control protocol (TCP).In this paper, we present and analyze a detailed burst reordering model for two widely applied burst assembly strategies: time-based and random selection. We apply the IETF reordering metrics and calculate explicitly three reordering metrics: the reordering ratio, the reordering extent metric and the TCP relevant metric. These metrics allow estimating the degree of reordering in a certain network scenario. They estimate the buffer space at the destination to resolve reordering and quantify the number of duplicate acknowledgements relevant for investigations on the transmission control protocol.We show that our model reflects the burst/packet reordering pattern of simulated OBS networks very well. Applying our model in a network emulation scenario, enables investigations on real protocol implementations in network emulation environments. It therefore serves as a substitute for extensive TCP over OBS network simulations with a focus on burst reordering.     

A visual language for modeling multiple perspectives of business process compliance rules

A fundamental challenge for enterprises is to ensure compliance of their business processes with imposed compliance rules stemming from various sources, e.g., corporate guidelines, best practices, standards, and laws. In general, a compliance rule may refer to multiple process perspectives including control flow, time, data, resources, and interactions with business partners. On one hand, compliance rules should be comprehensible for domain experts who must define, verify, and apply them. On the other, these rules should have a precise semantics to avoid ambiguities and enable their automated processing. Providing a visual language is advantageous in this context as it allows hiding formal details and offering an intuitive way of modeling the compliance rules. However, existing visual languages for compliance rule modeling have focused on the control flow perspective so far, but lack proper support for the other process perspectives. To remedy this drawback, this paper introduces the extended Compliance Rule Graph language, which enables the visual modeling of compliance rules with the support of multiple perspectives. Overall, this language will foster the modeling and verification of compliance rules in practice.     

Word对象模型在操作题自动测评中的应用

为了实现高效公正的组卷和判卷,Office操作题自动测评系统应运而生。结合实际开发的Office操作题测评系统,以Word操作题测评为例,探讨自动测评的关键技术原理．并着重研究Word对象模型在测评技术中的具体应用。     

Packet reordering in high-speed networks and its impact on high-speed TCP variants

Several recent Internet measurement studies show that the higher the packet sending rate, the higher the packet-reordering probability. This implies that recently proposed high-speed TCP variants are more likely to experience packet reordering than regular TCP in high-speed networks, since they are designed to achieve much higher throughput than regular TCP in these networks. In this paper, we first study the characteristics of packet reordering in high speed networks. Second, we verify the impact of packet reordering on high speed TCP variants and evaluate the effectiveness of the existing reordering-tolerant TCP enhancements using simulations. Our simulation results demonstrate that high-speed TCP variants perform poorly in the presence of packet reordering, and existing reordering-tolerant algorithms can significantly improve the performance of high-speed TCP variants.     

孤立词语音识别算法性能研究与改进

文章针对特定人中小字表孤立词语音识别,以提高实用性为目的,对两种常用识别方法(VQ、DTW)的效果及其性能(识别速度和识别率)改善进行分析与探索,并通过对实验数据的讨论,提出了一些有效的改进方法。     

基于词频统计的中文分词的研究

论文介绍了一个基于词频统计的中文分词系统的设计和实现。通过这个系统,可以将输入的连续汉字串进行分词处理,输出分割后的汉语词串,一般是二字词串,并得到一个词典。词典中不重复地存储了每次处理中得到的词语,以及这些词语出现的频率。这个系统选用了三种统计原理分别进行统计:互信息,N元统计模型和t-测试。文中还对这三种原理的处理结果进行了比较,以分析各种统计原理的统计特点,以及各自所适合的应用场合。     

Strengthening the case for pair programming

The software industry has practiced pair programming (two programmers working side by side at one computer on the same problem) with great success for years, but people who haven't tried it often reject the idea as a waste of resources. The authors demonstrate that using pair programming in the software development process yields better products in less time-and happier, more confident programmers     

面向Web的Word文件输出技术研究与实现

为了满足特定用户对于Web系统Word文件输出流格式的需求,在J2EE平台上对Java2Word类库进行二次开发,给出一种可自定义的输出Word文件格式的设计思路和方法。在数字化档案馆系统实际应用表明,输出效果良好,方便用户使用。     

基于词内部模式的新词识别

提出一种基于词内部模式的新词识别算法,该算法在重复串查找的基础上,结合词内部模式的特征提出改进位置成词概率和首尾单字成词概率的加权,依次判断互信息、邻接类别等统计量,对新词进行识别。通过不同的实验对比发现,该算法在一定程度上能有效提取新词。     

Anthropometry of the Singaporean and Indonesian populations

This research collected anthropometric data of the Singaporean and Indonesian populations. The data were mainly from university students. In total, 245 male and 132 female subjects from Indonesia and 206 male and 109 female subjects from Singapore were measured. The Singapore data were divided into three sub-groups, comprising Singapore overall, Singapore citizens, and the Chinese ethnic sub-group. The Indonesians data were divided into two sub-groups, comprising Indonesia citizens and Indonesia Chinese. This study used 36 measurement dimensions. The authors made a comparison with previous anthropometric data collected in 1990 of over a thousand Singaporeans.The main contributions of this study are: i) an updated anthropometric database of Singaporeans and Indonesians, ii) a comparison of the two samples obtained, and iii) a projection of dimensional changes over time from comparing past to more recent anthropometric data. Statistical analyses show that Singaporeans (both male and female) tend to have larger dimensions than Indonesians in general. In addition, the data reveal the current sample to be significantly larger on more than 50 percent of the dimensions measured, for both males and females.In providing instances of possible application, the Body Mass Index (BMI) of all sub-groups was calculated. The results show both samples to have normal indexes with BMIs in the range of 18.5–25.0. This paper presents also an empirical estimation of unknown anthropometric characteristics using the Ratio Scaling Method. The purpose is to estimate uncollected anthropometric data based on a given scaling dimension. Overall, the reported anthropometric data and analyses can be used as relevant consideration in product and systems design.

Relevance to industry

The findings of this study indicate differences between Singaporean and Indonesian anthropometry in the citizen and Chinese sub-groups. The utilization of an updated anthropometric database that incorporates geographical origin and ethnic group is useful. Product designers would be able to cater to a wider range of target users.